{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ungraded Lab:  Multiclass Classification: One-vs-all\n",
    "\n",
    "One vs All is one method for selection when there are more than two categories.\n",
    "![pic](./figures/onevsmany.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outline\n",
    "- [Tools](#tools)\n",
    "- [Dataset](#dataset)\n",
    "- [One vs All Implementation](#ova)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Classification: One-vs-all (OVA)\n",
    "In this lab, we will explore how to use the One-vs-All method for classification when there are more than two categories.This technique is an extention of two class or binomial logistic regression that we have working with. \n",
    "\n",
    "In binomail logistic regression, we train a model to classify samples that are in a class or not in a class. One-vs-All(OVA) extends this method by training $n$ models. Each model is responsible for identifying one class. A model for a given class is trained by recasting the training set to identify one class as positive and all the rest as negative. To make predictions, an example is processed by all $n$ models and the model with the largest prediction output is selected.\n",
    "\n",
    "In this lab, we will build a OVA classifier.\n",
    "## Tools \n",
    "- We will utilize our previous work to build and train models. These routines are provided. \n",
    "- Plotting decision boundaries and datasets is helpful. Producing these plots is quite involved so helper routines are provided below.\n",
    "- We will create a multi-class data set. Popular [`SkLearn`](https://scikit-learn.org/stable/) routines are utilized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lab_utils import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot  multi-class training points\n",
    "def plot_mc_data(X, y, class_labels=None, legend=False):\n",
    "    classes = np.unique(y)\n",
    "    for i in classes:\n",
    "        label = class_labels[i] if class_labels else \"class {}\".format(i)\n",
    "        idx = np.where(y == i)\n",
    "        plt.scatter(X[idx, 0], X[idx, 1],  cmap=plt.cm.Paired,\n",
    "                    edgecolor='black', s=20, label=label)\n",
    "    if legend: plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These routines are provided but reviewing their operation is instructive. Plotting routines often make use of many esoteric but useful numpy routines. Plotting decision boundaries makes use of `matplotlib's` contour plot. A contour plot draws a line at boundary of a change in values. That capability is used to delineate changes in decisions. Briefly, the routine has 3 steps:\n",
    "- create a fine mesh of locations in a 2-D grid. Build an array of those points.\n",
    "- make predictions for each of those points. In this case, this includes the vote for the best prediction.\n",
    "- plot the mesh vs the predictions(`Z`) using a contour plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot a multi-class decision boundary\n",
    "def plot_mc_decision_boundary(X,nclasses, Models , class_labels=None, legend=False):\n",
    "\n",
    "    # create a mesh to points to plot\n",
    "    h = 0.1  # step size in the mesh\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    points = np.c_[xx.ravel(), yy.ravel()]\n",
    "\n",
    "    #make predictions for each point in mesh\n",
    "    z = np.zeros((len(points),nclasses))\n",
    "    Z = predict_mc(points,Models)\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    #contour plot highlights boundaries between values - classes in this case\n",
    "    plt.figure()\n",
    "    plt.contour(xx, yy, Z, colors='g') \n",
    "    plt.axis('tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're providing the routines which you have developed in previous labs to create and fit/train a model. Feel free to replace these with your own versions. (Keep a copy of the original just in case.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_gradient(X, y, w):\n",
    "    \n",
    "    m, n = X.shape\n",
    "    f = sigmoid(np.matmul(X, w))\n",
    "    dw = (1/m)*np.matmul(X.T, (f - y))\n",
    "\n",
    "    return dw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(X, y, w):\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    f = sigmoid(X @ w)\n",
    "    total_cost = (1/m)*(np.dot(-y, np.log(f)) - np.dot((1-y), np.log(1-f)))\n",
    "    \n",
    "    return total_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_descent(X, y, w, cost_function, gradient_function, alpha, num_iters): \n",
    "    \"\"\"\n",
    "    Performs gradient descent to learn w. Updates w by taking \n",
    "    num_iters gradient steps with learning rate alpha\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array_like\n",
    "        Shape (m, n+1) \n",
    "    \n",
    "    y : array_like\n",
    "        Shape (m,) \n",
    "    \n",
    "    w : array_like\n",
    "        Initial values of parameters of the model\n",
    "        Shape (n+1,)\n",
    "        \n",
    "    cost_function : function\n",
    "        Function that is used as cost function for optimization\n",
    "        Takes in parameters X, y, w\n",
    "        \n",
    "    gradient_function : function\n",
    "        Function that returns the gradient update at each step for \n",
    "        parameters w\n",
    "        Takes in parameters X, y, w\n",
    "        \n",
    "    alpha : float\n",
    "        Learning rate\n",
    "           \n",
    "    num_iters : int\n",
    "        number of iterations to run gradient descent\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    w : array_like\n",
    "        Shape (n+1,)\n",
    "        Updated values of parameters of the model after\n",
    "        running gradient descent\n",
    "        \n",
    "    J_history : array_like\n",
    "        Output of cost function at each iteration\n",
    "    \"\"\"\n",
    "    \n",
    "    # number of training examples\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    # An array to store cost J at each iteration\n",
    "    J_history = np.zeros((num_iters, 1))\n",
    "    \n",
    "    for i in range(num_iters):\n",
    "        \n",
    "        # Save cost J at each iteration\n",
    "        J_history[i] = cost_function(X, y, w)\n",
    "        \n",
    "        # Calculate the gradient and update the parameters\n",
    "        gradient = gradient_function(X, y, w)\n",
    "        w = w - alpha * gradient\n",
    "          \n",
    "        # Print cost every 1000 iterations\n",
    "        if i%1000 == 0:\n",
    "            print(\"Cost at iteration %d: %f\" % (i, J_history[i]))\n",
    "        \n",
    "    return w, J_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X, w): \n",
    "    \"\"\"\n",
    "    Predict whether the label is 0 or 1 using learned logistic\n",
    "    regression parameters w\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array_like\n",
    "        Shape (m, n+1) \n",
    "    \n",
    "    w : array_like\n",
    "        Parameters of the model\n",
    "        Shape (n+1, 1)\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    p: array_like\n",
    "        Shape (m,)\n",
    "        The predictions for X using a threshold at 0.5\n",
    "    \"\"\"\n",
    "    # number of training examples\n",
    "    m = X.shape[0]   \n",
    "    p = np.zeros(m)\n",
    "   \n",
    "    for i in range(m):\n",
    "        f_w = sigmoid(np.dot(w.T, X[i]))\n",
    "        p[i] = f_w >=0.5\n",
    "    \n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='dataset'></a>\n",
    "##  Dataset\n",
    "Below, we use an `SkLearn` tool to create 3 'blobs' of data. Using NumPy's [`np.unique`](https://numpy.org/doc/stable/reference/generated/numpy.unique.html), we can look at the number and values of the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make 3-class dataset for classification\n",
    "centers = [[-5, 0], [0, 4.5], [5, -1]]\n",
    "X_orig, y_train = make_blobs(n_samples=500, centers=centers, cluster_std=0.85,random_state=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mc_data(X_orig,y_train,[\"blob one\", \"blob two\", \"blob three\"], legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique classes [0 1 2]\n",
      "shape of X_orig: (500, 2), shape of y_train: (500,)\n"
     ]
    }
   ],
   "source": [
    "# show classes in data set\n",
    "print(f\"unique classes {np.unique(y_train)}\")\n",
    "# show shapes of our dataset\n",
    "print(f\"shape of X_orig: {X_orig.shape}, shape of y_train: {y_train.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ova'></a>\n",
    "##  One Vs All Implementation\n",
    "\n",
    "We'll implement the OVA algorithm in three step.\n",
    "- create and train three 'models'. Each trained to select one of the three classes.\n",
    "- create a routine that will use the models to make predictions and select the best answer.\n",
    "- plot the decision boundary using the prediction routine.\n",
    "\n",
    "### Step 1: Create and Train 3 models.\n",
    "The steps involved will be familiar from past labs utilizing gradient descent. For each class:\n",
    "- extend the data set with a column of ones to account for $w_0$ (this is provided)\n",
    "- create `w_init`, initial values from the parameters. We have 3 parameters.\n",
    "- call gradient descent. alpha=1e-2 and num_iters=1000 works well. This returns $w$ and Cost history. We won't need cost history here. $w$ is our model which we will store in an array.\n",
    "- call predict with the training data and our model ($w$) to see how good our model is. Note, predict expects the original, non-extended examples (`X_orig`).\n",
    "\n",
    "Below there is a for loop over each of the classes. \n",
    "- creates an target array with the current class set to one and all others set to zero.\n",
    "     - `yc = (y_train==classes[i]) + 0`\n",
    "- plots this interpretation of the data\n",
    "- your code\n",
    "- plots the predicted values\n",
    "Replace `None` with your code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Hints</b></font></summary>\n",
    "\n",
    "```\n",
    "classes=[0,1,2]\n",
    "m,n = X_orig.shape\n",
    "# extend the data with a column of ones\n",
    "X_train = np.hstack([np.ones((m,1)), X_orig])\n",
    "# storage for our models (w), one column per class\n",
    "W_models = np.zeros((n+1,len(classes)))   \n",
    "\n",
    "plt.figure(figsize=(14, 14))             \n",
    "for i in classes:\n",
    "    ax = plt.subplot(3,2, 2*i + 1)\n",
    "    yc = (y_train==classes[i]) + 0\n",
    "    plot_mc_data(X_orig, yc,legend=True); plt.title(f\"Training Classes, class {i}\"); \n",
    "    ### START CODE HERE ### \n",
    "    w_init = np.zeros((3,))   \n",
    "    W_models[:,i],_ = gradient_descent(X_train, yc, w_init, compute_cost, compute_gradient,\n",
    "                                       alpha = 1e-2, num_iters=1000) \n",
    "    pred =  predict(X_train, W_models[:,i]) \n",
    "    ### END CODE HERE ###         \n",
    "    \n",
    "    ax = plt.subplot(3,2, 2*i + 2)\n",
    "    plot_mc_data(X_orig,pred,legend=True); plt.title(\"Predicted Classes\");\n",
    "plt.show   \n",
    "```\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost at iteration 0: 0.693147\n",
      "Cost at iteration 0: 0.693147\n",
      "Cost at iteration 0: 0.693147\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes=[0,1,2]\n",
    "m,n = X_orig.shape\n",
    "# extend the data with a column of ones\n",
    "X_train = np.hstack([np.ones((m,1)), X_orig])\n",
    "# storage for our models (w), one column per class\n",
    "W_models = np.zeros((n+1,len(classes)))   \n",
    "\n",
    "plt.figure(figsize=(14, 14))             \n",
    "for i in classes:\n",
    "    ax = plt.subplot(3,2, 2*i + 1)\n",
    "    yc = (y_train==classes[i]) + 0\n",
    "    plot_mc_data(X_orig, yc,legend=True); plt.title(f\"Training Classes, class {i}\"); \n",
    "    ### START CODE HERE ### \n",
    "    ### BEGIN SOLUTION ###\n",
    "    w_init = np.zeros((3,))   ##None\n",
    "    W_models[:,i],_ = gradient_descent(X_train, yc, w_init, compute_cost, compute_gradient,##None\n",
    "                                       alpha = 1e-2, num_iters=1000) \n",
    "    pred =  predict(X_train, W_models[:,i]) ##None\n",
    "    ### END SOLUTION ### \n",
    "    ### END CODE HERE ###         \n",
    "    \n",
    "    ax = plt.subplot(3,2, 2*i + 2)\n",
    "    plot_mc_data(X_orig,pred,legend=True); plt.title(\"Predicted Classes\");\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>\n",
    "    <b>**Expected Output**:</b>\n",
    "</summary>\n",
    "\n",
    " ![asdf](./figures/C1W3_trainvpredict.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have trained our 3 models we will write a routine to select the best prediction. \n",
    "-Step 1: Given $X$ and matrix $W$, perform a prediction resulting in three predictions. This can be one line if vectorised. ![pic](./figures/C1W3_XW.PNG)  \n",
    "-Step 2: use `np.argmax()` to return the **class** of the prediction with the highest value. Note that class is one of [0,1,2] and the index returned by `np.argmax` is, conveniently also [0,1,2]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Hints</b></font></summary>\n",
    "\n",
    "```\n",
    "def predict_mc(X,W):\n",
    "    \"\"\"\n",
    "    Adds a column of ones to X and computes n predictions and selects the best.\n",
    "    Args:\n",
    "      X : (array_like Shape (m,n)) feature values used in prediction.  \n",
    "      W : (array_like Shape (n,c)) Matrix of parameter. Each column represents 1 model.c models\n",
    "    Returns\n",
    "      sclass: (array_like Shape (m,1)) The selected class the values belong in. Values 0 to c.\n",
    "    \"\"\"\n",
    "    X = np.hstack([np.ones((len(X),1)), X])\n",
    "    ### START CODE HERE ### \n",
    "    P = X @ W\n",
    "    sclass = P.argmax(axis=1)\n",
    "    ### END CODE HERE ### \n",
    "\n",
    "    return(sclass)  \n",
    "```\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_mc(X,W):\n",
    "    \"\"\"\n",
    "    Adds a column of ones to X and computes n predictions and selects the best.\n",
    "    Args:\n",
    "      X : (array_like Shape (m,n)) feature values used in prediction.  \n",
    "      W : (array_like Shape (n,c)) Matrix of parameter. Each column represents 1 model.c models\n",
    "    Returns\n",
    "      sclass: (array_like Shape (m,1)) The selected class the values belong in. Values 0 to c.\n",
    "    \"\"\"\n",
    "    X = np.hstack([np.ones((len(X),1)), X])\n",
    "    ### START CODE HERE ### \n",
    "    ### BEGIN SOLUTION ###  \n",
    "    P = X @ W\n",
    "    sclass = P.argmax(axis=1)\n",
    "    ### END SOLUTION ###  \n",
    "    ### END CODE HERE ### \n",
    "\n",
    "    return(sclass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we can make a prediction for any point, we can now produce a plot with the decision boundary shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the decison boundary. Pass in our models - the w's assocated with each model\n",
    "plot_mc_decision_boundary(X_orig,3, W_models)\n",
    "plt.title(\"model decision boundary vs original training data\")\n",
    "\n",
    "#add the original data to the decison bounaryd\n",
    "plot_mc_data(X_orig,y_train,[\"blob one\", \"blob two\", \"blob three\"], legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>\n",
    "    <b>**Expected Output**:</b>\n",
    "</summary>\n",
    "\n",
    "![sdf](./figures/C1W3_boundary.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There you are! You have now build a Multi-Class classifier.\n",
    "\n",
    "Lets try another case. We'll just move the blobs around a bit:\n",
    "## Second Test Case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make 3-class dataset for classification\n",
    "centers = [[-5, 0], [0, 1], [5, -1]]\n",
    "X_orig, y_train = make_blobs(n_samples=500, centers=centers, cluster_std=1.2,random_state=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mc_data(X_orig,y_train,[\"blob one\", \"blob two\", \"blob three\"], legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique classes [0 1 2]\n",
      "shape of X_orig: (500, 2), shape of y_train: (500,)\n"
     ]
    }
   ],
   "source": [
    "# show classes in data set\n",
    "print(f\"unique classes {np.unique(y_train)}\")\n",
    "# show shapes of our dataset\n",
    "print(f\"shape of X_orig: {X_orig.shape}, shape of y_train: {y_train.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examaning the plot above, do you see any potential issues with our current approach?\n",
    "\n",
    "Piece together the pieces from above, or create subroutines to create a decision boundary diagram like the one in the first example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Hints</b></font></summary>\n",
    "\n",
    "```\n",
    "classes=[0,1,2]\n",
    "m,n = X_orig.shape\n",
    "X_train = np.hstack([np.ones((m,1)), X_orig])\n",
    "W_models = np.zeros((n+1,len(classes)))   # stores our models\n",
    "             \n",
    "for i in classes:\n",
    "    y_ = (y_train==classes[i]) + 0\n",
    "    #plot_mc_data(X_orig, y_,legend=True); plt.title(\"Original Classes\"); plt.show()\n",
    "    w_init = np.zeros((3,))\n",
    "    W_models[:,i],_ = gradient_descent(X_train, y_, w_init, compute_cost, compute_gradient, 1e-3, 10) \n",
    "    pred =  predict(X_train, W_models[:,i])\n",
    "    #plot_mc_data(X_orig,pred,legend=True); plt.title(\"Predicted Classes\"); plt.show()\n",
    "    \n",
    "#plot the decison boundary. Pass in our models - the w's assocated with each model\n",
    "plot_mc_decision_boundary(X_orig,3, W_models)\n",
    "plt.title(\"model decision boundary vs original training data\")\n",
    "\n",
    "#add the original data to the decison boundary\n",
    "plot_mc_data(X_orig,y_train,[\"blob one\", \"blob two\", \"blob three\"], legend=True)\n",
    "plt.show() \n",
    "```\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rewrite code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>\n",
    "    <b>**Expected Output**:</b>\n",
    "</summary>\n",
    "\n",
    "![asdf](./figures/C1W3_example2.PNG)\n",
    "    \n",
    "We will study logistic regression with polynomial features in the next lab. That will allow us to handle situations where purely linear solutions are not enough."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook made was informed by an example at scikit-learn.org. The author was Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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